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Paper currency recognitionPaper Currency Recognition System using Characteristics Extraction and Negatively Correlated NN Ensemble Paper currency recognition are significant in many applications. The requirements for an automatic banknote recognition system offered many researchers to build up a robust and dependable technique. Speed and precision of processing are two vital factors in such systems. Of course, the precision may be much significant than the speed. The designed system should have an important precision in detecting torn or worn banknotes. The currency recognition is one of the significant application domains of artificial neural networks. This paper discusses the ENN for currency recognition. NCL was used for the training of the network. The use of NCL is to produce the diversity among the individual networks in ensemble. The final decision of the network is taken from voting among the individual NN.

Literature ReviewPresently ,there are a number of methods for paper currency recognition: Using symmetrical masks technique for recognizing paper currency in any direction. Other method:1. The edges of patterns on a paper currency are spotted.2. In the next step, paper currency is divided into N equal parts along vertical vector.3. Then, for each edge in these parts the number of pixels is added and fed to a three-layer, back propagation neural network.4. In this process, to conquer the problem of recognizing dirty worn banknotes, the following linear function is used as a pre-processor:

f(x) = Fax + Fb (1)

where x is the given (input) image in gray scale, f(x) is the resultant image;and Fa= 3 , Fb = -128 and N =50

other method use infrared or ultraviolet spectra may be used for discriminating between genuine and counterfeits notes. Most of paper currency recognition techniques use a single multilayer feed-forward NN for the recognition. These uses edge detection technique for feature extraction. This reduces the network size. For new notes feature extraction from edge detection is simple. But for the noisy notes it is very difficult. If a network takes a false classification it will be not practical. So a single network is not reliable enough. Therefore ENN is presented in this paper to solve this problem.

Characteristics extraction Size The first phase of recognition in the algorithm considers size of the banknote. The edges of banknotes are generally worn and torn due to circulations. Hence, its size is reduced, or even is increased slightly in rejoining the torn banknotes. the size condition in the decision tree is presented as: | x x0 |< dx & | y y0 |< dy (2) Where x0 and y0 are size of the testing paper currency, and x and y are size of the reference paper currency.dx and dy are vertical and horizontal directions.

ColorImage of the banknote is transformed to an image in gray scale]. Then the gray scale level is reduced to have a significant judgment about the background color.

TextureFor recognizing the template, Markov chain concept is used in representing random phenomenon. A random process {xk, k = 0, 1, 2....} is called a Markov chain if the possibility value in state xn+1 depends on just the possible value in state xn , that is:

P(xn+1 = | xn = , xn-1 = n-1 ,..,x0 = 0 ) = P(xn+1 = |xn = )

This possibility can be shown by Pij. The state space of a Markov chain can be shown in a matrix that is:

P=

where n is the number of states in the chain.

Steps for paper currency recognition :1. Banknote Size is calculated. If its size satisfies equation (2) it is considered as a possible true banknote.2. The banknote image histogram is calculated.3. The transition matrices (Nx and Ny) are calculated then, the main diagonal elements of the matrices (namely Dx and Dy) are taken out as a feature for distinguishing between different denominations.4. The paper currency under observation is assigned to a denomination class if the Euclidean distances between the main diagonal elements of its transition matrices (Dx and Dy) and the main diagonal elements of the corresponding matrices of the reference banknote (DRx and DRy ) are smaller than a predefined value.5. At the end, the computed histogram in stage 2 is compared with the histogram of the winner class in stage 4. If the Euclidian distance between the two histograms is larger than the predefined value, the banknote is assigned to an unknown class.

An approach using negative correlation learning NCL is used for the training of the network. The use of NCL is to produce the diversity among the individual networks in ensemble.

Assume a training set S of size N. S = {(x (l), d(l), x(2), d(2)),..(x(N), d(N))} Where x is the input vector and d is the desired result. Consider approximating d by forming an ensemble whose result F(n) is the average in the component NN result Fi(n)

(5)

Where M and n refer to the number of NN in ensemble and training pattern, respectively. The error function Ei of the network i in NCL is given by the following eq (6).

(6)

(7)

Where Ei(n) is the value of the error function of the network i for the nth training pattern. The first term of (7) is the empirical risk function of the network i. In the second term, Pi is a correlation penalty function is given by eq (8).

(8)

The partial derivative of Ei(n) with respective to the output network i on the nth training pattern is

= = = (1-) (Fi(n) d(n)) + (F(n) d(n)) The NCL is a simple extension to the standard Back-propagation algorithm [8]. In fact, the only alteration that is needed is to compute an extra term of the form for the ith network. During the training process, the entire ensemble interacts with each other through their penalty terms in the error functions. Each network i minimizes not only the difference between Fi(n) and d(n) , but also the difference between F(n) & d(n). That is, negative correlation learning considers errors what all other networks have learned while training a network.

Comparative study of different paper currency and coin recognition method

Currency has great importance in day to day life so currency recognition is a great area of interest .We can conclude that image processing is the most popular effective method of currency recognition .Image processing based currency recognition technique consists of:1. Image acquisition (using cameras or scanners).2. Pre-processing (features extracting).3. Recognition of currency.

Currency can be of two types:1. coin currency.2. paper currency.

Coin currency recognition methodCoin recognition by method to designed a neural network (NN) by using a genetic algorithm(GA) and simulated annealing.(2000) Effective in a small number of input signals. Small size neural network is developed. Low cost. Accuracy is 99.68%.

Coin-o-omatic(2006) Designed to perform reliable classification of heterogeneous coin collection. Uses combination of coin photographs and sensor information in classification. Perform automatic classification of coin in :1. Segmentation.2. Feature extraction (using edge angle-distance distribution).3. Pre-selection.4. Classification (nearest-neighbor).5. Verification. Accuracy is 72%.

Image abstraction and spiral decomposition based system (2007) Obtain abstract image (considering strong edges) from the original image. Features extraction (spiral decomposition method) Spiral distribution of pixels is the key concept that enables the system to recognize the similarity between a full color multicomponent coin images. No cost in image segmentation.

Image based approach using Gabor wavelet (2009) Extract features for local texture representation. Divide image into small section (using concentric ring structure). Statistics of Gabor coefficients within each section is concatenated into a feature vector for whole image. Matching between two coin image (via Euclidean distance and nearest neighbor). Accuracy of 74.27%.Paper currency recognition

Paper currency recognition for euro using three layer perception and radial basis function (RBF) (2003) Used three layer perception for classification and RBF for validation. RBF network has a potential to reject invalid data.

Currency recognition using ensemble neural network (ENN) for TAKA (bangladesi currency) (2010) Neural network in ENN is in fact a classifier trained via negative correlation learning (NCL). The currency image converted to gray scale and then compressed, each compressed pixel is an input to the network. ENN is useful in different types of currency.

Block LBP(local binary pattern) for characteristics extraction in paper currency recognition (2010) Is improved version of LBP. Works in two phases:1. Model creating : Preparing template. Features extracting.2. The verification High recognition speed. High classification accuracy.

Side invariance paper currency recognition based on matching input note image with database of note image (2012)Overall process are: Image acquisition and segmentation. Dimension matching. Template matching. Decision making.

Recent developments in paper currency recognition system

Main steps in any currency recognition are:Matching algorithmFeature extractingCurrency note localization(edge detection and segmentationImage aquistion

Final output decision

Image acquisition: getting currency image by digital camera or scanner. Edge detection: identifying the points at which the image brightness changes sharply. Image segmentation: dividing the image into its constituent regions or object. Feature extraction: one of challenging tasks, identify the unique and distinguishing features of each denomination under condition like old ,torn and worn notes. Matching algorithm: classifies the currency notes.Potential applications1. Assisting blind and visually impaired people.2. Distinguishing original note from counterfeit currency.3. Automatic selling goods.4. Banking service and applications.Related work Image acquisition : is the creation of digital image. Image pre-processing: Enhance some image features1. Image adjusting: reducing the image size.2. Image smoothening: by applying mask on the image higher is the size of mask, more is the smoothing. Edge Detection: is the fundamental tool in feature detection and extraction, reflects sharp intensity in colors and identifies object boundaries using Sobel,Prewitt,Robert and Canny. Canny is more powerful as it can detect true weak edges. Boundary Subtraction: detect and recognize the note, black pixels touching the boundary of the image were regarded as background, as note had a white background. Feature Extraction: includes feature of serial numbers of currency notes,effects on design and performance of the classifier. Evaluation algorithm: after getting features of currencies which then will be recognized by effective recognition system called classifier, one of the most common techniques is Artificial Neural Network (ANN). The Neural Network (NN) consists of three layers: input layers, hidden layers and output layers. Acquired EGB image convert to gray scale. Edge detection done on whole gray scale. Paper currency characteristics are cropped and segmented, and then extracted. Comparing these with the original pre-stored image in the system. If matching it is genuine otherwise it is counterfeit. The neural network evaluate the hue and saturation threshold of input image ,if the Neural Network threshold is less than current image threshold it is genuine otherwise it is counterfeit.

Other work (2011) They used image histogram based on plenitude of different colors of notes calculated and compared with reference note. Also they used Markov chain concept to model texture of paper currency as a random process. Finally they used Ensemble Neural Network (ENN) with negative correlation in classification. ENN has better performance than single network.Discussion Artificial Neural Network based currency classification is the most frequently used method like Feed Forward Network, Back Propagation Neural Network, RBF network and ENN. There are also some models developed by researchers like Markov chain.

A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine

The needs for an automatic banknote recognition system encouraged many researchers to develop fast, accurate, reliable and robust technique which also be able to adapt to high noise.this task can be:

1. Extracting features from paper currency images that vary from each denomination.2. Using these features in an intelligent system for recognition.

Recognition systems should be able to recognize paper currency from each side and torn or worn banknotes.these systems depends on currency note characteristics.This paper presents a new paper currency recognition technique that independent to the number of paper currency classes using texture characteristics of paper currency. Texture modeled using Hidden Markov Model(HMM).

Existing paper currency methods: Recognition paper currency in any direction using symmetrical masks: Compute sum of non-masked pixels value and fed to a neural network. Uses two sensors one for front and the other for back of paper currency, but only the front image is the criterion for decision. Other research: Detect edge of patterns on paper currency. Divide paper currency into N equal parts vertically. For each edge of parts number of pixels counted and fed to a three-layer, back propagation neural network. Overcoming the dirty worn banknotes by function:

f(x) = Fax+Fd

where x is the input image in gray scale, f(x) is the output image and selecting Fa=3, Fd=-128, N=50

Wiener filterIt is a filter used to reduce the effect of dirt through improving image lightness.Wiener filter estimate local mean and variance around each pixel as:

where is the N-by-M local neighborhood of each pixel in the image then,a pixel-wise wiener filter using above estimation has been created:

Where v2 is the noise variance. if the noise variance not given wiener uses the average of all estimated variance.

(a)

(b)

(c)

(d)Fig 1:Using the Wiener filter to reduce the dirt from the worn banknote. a)Original image before filtering, b)Obtained image after filtering. c)Worn image before filtering d)Worn image after filtering.

Fourier-mellin TransformIt is a powerful tool for image recognition as: its resulting spectrum is invariant in rotation, translation and scale. The Fourier Transform (FT) itself is translation invariant and its conversion to log-polar coordinates converts the scale and rotation differences to vertical and horizontal offsets that can be measured. A second FFT, called the Mellin transform gives a transform-space image that is invariant to translation, rotation and scale . For an input image, I[m,n], the Fourier-Mellin transform is defined as below:

(a)

(b)

(c) Fig 2:Using the Fourier-Mellin transform to reduce the effect of rotation of the banknote: a) Original image, b) Rotated image c) Output image after applying Fourier-Mellin transform.

Hidden Markov Model (HMM) By using the hidden Markov Model the texture of a banknote is modeled as a random process. A random process is called a Markov chain if the possibility value in state depends on just the possible value in state that is:

P=

Where n is the number of states in a chain. In a discrete time Markov chain, the possibility value of different states in the matrix is computed as follow:

Where is the number of transitions from state i to state j. Considering the above equation , matrix can be multiplied by the factor

In order to obtain the below equation, this matrix is used to differentiate between textures in different denominations. We can scan the banknotes from top to bottom and from left to right to obtain the transition matrix across the row (Nx) and across the column (Ny)

N= =

An image is recognized by the value of its pixels at different places, the way that adjacent pixels vary can also be used to distinguish different images. Considering a paper currency image as an matrix that is shown in table1, the value of each pixel like ij can be considered as one state.It is clear that using the lower numbers of gray scales leads to a lower computational load. Therefore, in this paper the main diagonal values of the matrix N have been used. Each element of the main diagonal value represents the number of times that the corresponding value repeated in adjacent pixels. The transition matrix (N) for a 5 euro banknote shown in Figure 3 that has been quantized in 11 level gray scales is shown in Table 1. In this table, for example, the 39 represents the number of times the values of adjacent pixels are between 0 and 22. Our investigations indicate that the main diagonal values are sufficient to distinguish different denominations. The main diagonal values of seven different euro banknotes are illustrated in Table 2. As it shows, all of the banknotes are distinguishable using the main diagonal value. It should be noted that the dominant colors of the banknote is recognizable using the main diagonal values. Our investigations indicate that using the main diagonal values is robust against worn banknotes. To show the robustness of this feature, the main diagonal values of clean and dirty 5 euro banknote are shown in table3. Because of the monotonic impurity in dirty banknotes, the effect of the impurity on the main diagonal values is negligible.

Fig 3: Image of 5 Euro banknote

Table 1. Transition matrix for a 5 Euro banknote45 19 16 8 7 5 5 4 4 0 0

35 402 238 50 38 25 9 16 21 4 0

9 235 871 356 114 90 48 25 15 9 5

7 50 337 1553 459 183 116 39 33 17 5

3 29 150 556 1938 700 231 100 74 35 12

2 26 59 157 876 7077 1726 240 125 70 28

5 19 49 51 222 1860 3406 467 213 126 26

6 52 26 26 75 256 486 793 675 331 39

1 5 13 23 46 107 198 634 6726 2098 60

0 1 15 17 48 67 198 409 1978 3769 80

0 0 3 2 5 16 21 38 67 103 49

5euro 45 402 871 1553 1938 7077 3406 793 6726 3769 49

10euro 0 35 609 585 512 2284 2646 6804 12852 2760 5

20euro 9 451 1359 461 1007 4821 2025 5372 11825 245 8

50euro 0 261 985 777 2122 4753 6051 6806 19935 16754 19969

100euro 0 119 1405 910 1430 2503 5725 5648 8204 1427 34

200euro 0 83 811 768 678 913 1605 3287 5160 15485 90

500euro 0 98 1255 876 1429 2615 8082 2457 2412 8895 63

Table 2. Transition matrix for 7 types Euro banknotesOriginal 5 euro 45 402 871 1553 1938 7077 3406 793 6726 3769 49

Dirty 5 euro 39 398 813 1456 1892 6981 3355 721 6451 3689 24

Table 3. Transition matrix for original 5 Euro and dirty 5 euro

Finally from the four paper I have read I found:

Currency denomination recognition is one the active research topics at present , and this wide interest is due to: Monetary transaction is an integral part of our day to day activities. blind people particularly suffer in monetary transactions. They are not able to effectively distinguish between various denominations and are often deceived by other people. Also there are many applications that depends on currency recognition like:1. Assisting visually impaired people-2. Distinguishing original note from counterfeit currency-3. Automatic selling-goods-4. Banking Applications- Paper currency are coins or paper. paper is significant rather than coins as they become old early.

Methodology of any currency recognition system the main steps :

Final output decisionMatching algorithmFeature extractingCurrency note localization(edge detection and segmentationImage aquistion

Most of matching algorithm used Artificial Neural Network specially Ensemble Neural Network as its better performance than single Neural Network.Also the most common model technique is the Markov Chain.